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| 082 | 0 0 | ▼a 003/.54 ▼2 21 |
| 090 | ▼a 003.54 ▼b K99u | |
| 100 | 1 | ▼a Kyburg, Henry Ely, ▼d 1928- |
| 245 | 1 0 | ▼a Uncertain inference / ▼c Henry E. Kyburg, Jr. and Choh Man Teng. |
| 260 | ▼a Cambridge ; ▼a New York : ▼b Cambridge University Press, ▼c 2001. | |
| 300 | ▼a xii, 298 p. ; ▼c 27 cm. | |
| 504 | ▼a Includes bibliographical references and indexes. | |
| 650 | 0 | ▼a Uncertainty (Information theory) |
| 650 | 0 | ▼a Probabilities. |
| 650 | 0 | ▼a Logic, Symbolic and mathematical. |
| 700 | 1 | ▼a Teng, Choh Man. |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 003.54 K99u | Accession No. 111211226 (1회 대출) | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science.
This book presents a clear exposition of the approaches to the problem of uncertain inference.
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Table of Contents
Preface; 1. Historical background; 2. First order logic; 3. The probability calculus; 4. Interpretations of probability; 5. Nonstandard measures of support; 6. Nonmonotonic reasoning; 7. Theory replacement; 8. Statistical inference; 9. Evidential probability; 10. Semantics; 11. Applications; 12. Scientific inference.
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